Recent studies show that likely over 70% of human proteins can be phosphorylated ? therefore characterization of phosphorylation extents and dynamics is critical to understanding a broad range of biological and biochemical processes. To meet the needs for high-throughput screening of phosphorylation, technologies based on mass spectrometry are advancing rapidly, including use of metal-affinity enrichment as well as phospho-antibody enrichment to enhance the detection of phosphosites and quantification of phosphorylation levels. These technologies enable untargeted quantification of the phosphorylation levels of thousands of phosphosites in a given sample. Today, many labs are utilizing these technologies to comparatively characterize signaling landscapes by examining perturbations with drugs and knockdown approaches and assessing diverse phenotypes in cancer, infectious disease, and normal development. However, as compared to other sources of omic data (e.g., genomic, transcriptomic, and interactomic data), sharing of phospho-proteomic data, as well as its secondary and integrated analysis are relatively less common at present. For these reasons, phospho-proteomic data generated by different labs that capture phosphorylation dynamics in the context of diverse biological processes are not being utilized to their full potential. This project aims to develop computational methods that will use phosphorylation data from diverse studies and different labs to elucidate the dynamics of the interactions and post-translational modifications among relevant proteins, phosphosites, kinases, and phosphatases. For this purpose, we propose to construct co- phosphorylation networks by assessing the correlation (or statistical dependency in general terms) between pairs of phosphosites across a range of biological states, and develop algorithms and methods to analyze and utilize these networks to develop systems biology solutions to various problems. The proposed computational pipeline will introduce the notion of co-phosphorylation (co-P) networks to the scientific community, provide comprehensive methodologies for the construction, statistical assessment, and functional assessment of these networks, and validate their use in predictive tasks. Specifically, for experimental validation, we will use kinase inhibition studies in lung cancer cell lines and tissue samples from in vivo models of Alzheimer?s disease. The potential impact of this project is well beyond the results that will be generated by this project; utilization of co-P networks by a broad range of biomedical scientists will likely generate significant insights into the biology of cellular signaling and drive drug discovery for enhancing biomedical science.